In [1]:
!pip install yfinance==0.1.67
!mamba install bs4==4.10.0 -y
!pip install nbformat==4.2.0

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        mamba (1.4.2) supported by @QuantStack

        GitHub:  https://github.com/mamba-org/mamba
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Looking for: ['bs4==4.10.0']

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pkgs/r/noarch                                                 No change

Pinned packages:
  - python 3.7.*


Transaction

  Prefix: /home/jupyterlab/conda/envs/python

  All requested packages already installed

Requirement already satisfied: nbformat==4.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (4.2.0)
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In [2]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [3]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)
In [4]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

Question 1: Use yfinance to Extract Stock Data

Reset the index, save, and display the first five rows of the tesla_data dataframe using the head function. Upload a screenshot of the results and code from the beginning of Question 1 to the results below.

In [5]:
tesla = yf.Ticker('TSLA')
In [6]:
tesla_data = tesla.history(period="max")
In [7]:
tesla_data.reset_index(inplace=True)
tesla_data.head(5)
Out[7]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500 0 0.0
1 2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500 0 0.0
2 2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000 0 0.0
3 2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000 0 0.0
4 2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500 0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data

Display the last five rows of the tesla_revenue dataframe using the tail function. Upload a screenshot of the results.

In [8]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data = requests.get(url).text
In [9]:
soup = BeautifulSoup(html_data, 'html.parser')
soup.find_all("title")
Out[9]:
[<title>Tesla Revenue 2010-2022 | TSLA | MacroTrends</title>]
In [10]:
tesla_revenue = pd.DataFrame(columns = ["Date","Revenue"])

for table in soup.find_all('table'):
    if table.find('th').getText().startswith("Tesla Quarterly Revenue"):
        for row in table.find("tbody").find_all("tr"):
            col = row.find_all("td")
            if len(col) != 2: continue
            Date = col[0].text
            Revenue = col[1].text.replace("$","").replace(",","")
               
            tesla_revenue = tesla_revenue.append({"Date":Date, "Revenue":Revenue}, ignore_index=True)
In [11]:
tesla_revenue.dropna(axis=0, how='all', subset=['Revenue'])
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
In [12]:
tesla_revenue.tail(5)
Out[12]:
Date Revenue
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data

Reset the index, save, and display the first five rows of the gme_data dataframe using the head function. Upload a screenshot of the results and code from the beginning of Question 1 to the results below.

In [13]:
gme = yf.Ticker("GME")
In [14]:
gme_data = gme.history(period="max")
In [15]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[15]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 1.620128 1.693350 1.603296 1.691666 76216000 0.0 0.0
1 2002-02-14 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data

Display the last five rows of the gme_revenue dataframe using the tail function. Upload a screenshot of the results.

In [16]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data  = requests.get(url).text
In [17]:
soup = BeautifulSoup(html_data, 'html.parser')
In [18]:
gme_revenue = pd.read_html(html_data, match="GameStop Quarterly Revenue")[0]
gme_revenue.rename(inplace=True, columns={"GameStop Quarterly Revenue(Millions of US $)": "Date", "GameStop Quarterly Revenue(Millions of US $).1": "Revenue"})
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"",regex=True)
gme_revenue.dropna(inplace=True)
gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
In [19]:
gme_revenue.tail()
Out[19]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

Question 5: Plot Tesla Stock Graph

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph.

Upload a screenshot of your results.

In [20]:
make_graph(tesla_data, tesla_revenue, "Tesla")

Question 6: Plot GameStop Stock Graph

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph.

Upload a screenshot of your results.

In [21]:
make_graph(gme_data, gme_revenue, "GameStop")
In [ ]: